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Latin hypercube sampling (LHS) is a form of stratified sampling that can be applied to multiple variables. The method commonly used to reduce the number or runs necessary for a Monte Carlo simulation to achieve a reasonably accurate random distribution. Figure 1: Latin Hypercube DoE for M = 2 and N = 5. One way to solve this problem is to deï¬ne the generation of the Optimal Latin Hyper- cube design as an optimization problem. Audze and Eglais in [7] propose a method that uses the potential energy of the sample points to generate a uniform distribution of the points.
Mac; Windows 8, 8 RT and Modern UI. A Computer Code for Latin Hypercube Sampling (LHS). When combined with GIS software such as ArcView, LHS can improve sample. Latin-Hypercube designs There is also a wealth of information on the NIST website about the various design matrices that can be created as well as detailed information about designing/setting-up/running experiments in general.
Latin hypercube sampling (LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution. The sampling method is often used to construct computer experiments or for Monte Carlo integration.
The LHS was described by Michael McKay of Los Alamos National Laboratory in 1979.[1] An independently equivalent technique was proposed by EglÄjs in 1977.[2] It was further elaborated by Ronald L. Iman and coauthors in 1981.[3] Detailed computer codes and manuals were later published.[4]
Latin Hypercube Sampling Method
In the context of statistical sampling, a square grid containing sample positions is a Latin square if (and only if) there is only one sample in each row and each column. A Latin hypercube is the generalisation of this concept to an arbitrary number of dimensions, whereby each sample is the only one in each axis-aligned hyperplane containing it.
When sampling a function of N{displaystyle N} variables, the range of each variable is divided into M{displaystyle M} equally probable intervals. M{displaystyle M} sample points are then placed to satisfy the Latin hypercube requirements; note that this forces the number of divisions, M{displaystyle M}, to be equal for each variable. Also note that this sampling scheme does not require more samples for more dimensions (variables); this independence is one of the main advantages of this sampling scheme. Another advantage is that random samples can be taken one at a time, remembering which samples were taken so far.
In two dimensions the difference between random sampling, Latin Hypercube sampling, and orthogonal sampling can be explained as follows:
Value addition in floriculture pdf. Thus, orthogonal sampling ensures that the set of random numbers is a very good representative of the real variability, LHS ensures that the set of random numbers is representative of the real variability whereas traditional random sampling (sometimes called brute force) is just a set of random numbers without any guarantees.
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Active2 years, 7 months ago
I am running a simulation on a Linux server:
Here an example of what I am trying to do: Windows 7 64 ultimate serial key.
I can run this using a Linux server, after running the application I get a window (XQuartz, I am using a Mac) from which I can manipulate and run the experiment:
Latin Hypercube Sampling Example
Any ideas? Examples?
sdaza
sdazasdaza
1 Answer
To run AnyLogic (multi- or single-run) experiments in 'batch', the normal method is to export the experiment to a standalone Java application, which can then be run as needed from the command line. This is available only in the Professional edition; looks like you are using the Personal Learning Edition (PLE). Apdfpr pro 2 21 cracker.
Latin Hypercube Sampling R
Otherwise, AnyLogic is fundamentally a 'client-based' application. You might find some clever tricks to run it remotely as you suggest, but there is no 'intended' method, and you may be skirting along the edges of the license conditions.
Stuart RossiterStuart Rossiter
Latin Hypercube Sampling Software Mac Free
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